AirExplorer: visual exploration of air quality data based on time-series querying

Air pollution has become an important environmental issue, attracting more and more attention from many scholars and experts recently. Understanding air quality patterns in urban areas is essential for air pollution prevention and treatment. However, most existing studies usually cannot effectively capture air quality patterns from large-scale air quality data, due to lacking effective interaction approaches and intuitive methods that reveal sequential and multivariable information. In this paper, we present AirExplorer, a novel visual analysis system providing abundant interactive ways and intuitive views to help users explore the time-varying and multivariable patterns of air quality data. We design a time-embedded RadViz view that not only shows the relationship between data and multivariable attributes, but also puts the air quality temporal variations among the observation stations into perspective. Furthermore, we suggest a time-series querying algorithm, which combines hierarchical Piecewise Linear Representation and Dynamic Time Warping, to help users query time-series patterns of interest accurately by a sketch-based interaction. The experiment results based on the real dataset demonstrate that our method can help users understand the spatial-temporal multi-dimensional characteristics effectively and discover some potential laws of air quality patterns. AirExplorer with easy-to-use interactions can improve the efficiency of analyzing air quality data.